Detecting potentially occluded objects for autonomous vehicles

ABSTRACT

Aspects of the disclosure relate to controlling a vehicle having an autonomous driving mode. For instance, that the vehicle is approaching a crosswalk may be determined. A set of segments may be identified for the crosswalk. A set of potential occluded pedestrians may be generated. Each potential occluded pedestrian of the set is assigned a speed characteristic and a segment. The segments of the set of potential occluded pedestrians may be updated over time using the assigned speed characteristics. Sensor data from a perception system of the vehicle is received, and one or more potential occluded pedestrians an having an updated assigned segment corresponding to a segment that is visible to a perception system of the vehicle may be removed from the set of potential occluded pedestrians. After the removing, the set may be used to control the vehicle in the autonomous driving mode.

CROSS REFERENCE TO RELATED APPLICATIONS

The present application is a continuation of U.S. patent applicationSer. No. 17/734,375, filed May 2, 2022, which is a continuation of U.S.patent application Ser. No. 16/552,426, filed Aug. 27, 2019, issued asU.S. Pat. No. 11,354,912, the entire disclosures of which areincorporated herein by reference.

BACKGROUND

Autonomous vehicles, such as vehicles which do not require a humandriver when operating in an autonomous driving mode, may be used to aidin the transport of passengers or items from one location to another.Autonomous vehicles, such as vehicles which do not require a humandriver, may be used to aid in the transport of passengers or items fromone location to another. An important component of an autonomous vehicleis the perception system, which allows the vehicle to perceive andinterpret its surroundings using various sensors such as cameras, radar,lasers, and other similar devices. For example, autonomous vehicles mayuse the sensors to gather and interpret images and sensor data about itssurrounding environment, e.g., parked cars, trees, buildings, etc.Information from the perception system may be used by these vehicles tomake numerous decisions while the autonomous vehicle is in motion, suchas speeding up, slowing down, stopping, turning, etc.

Information from the perception system may also be combined with highlydetailed map information in order to allow a vehicle's computer tosafely maneuver the vehicle in various environments. This mapinformation may describe expected conditions of the vehicle'senvironment such as the shape and location of roads, traffic signals,and other objects. In this regard, the information from the perceptionsystem and detailed map information may be used to assist a vehicle'scomputer in making driving decisions involving how to maneuver thevehicle to avoid other objects in the roadway while obeying trafficlaws. This may be especially difficult where some objects in the roadwayare occluded by other objects.

Aspects of the disclosure provide a method of controlling a vehiclehaving an autonomous driving mode. The method includes determining, byone or more processors, that the vehicle is approaching a crosswalk;identifying, by the one or more processors, a set of segments for thecrosswalk; generating, by the one or more processors, a set of potentialoccluded pedestrians, each potential occluded pedestrian of the setbeing assigned a speed characteristic defining a speed of the potentialoccluded pedestrian and an assigned segment of the set of potentialoccluded pedestrians; updating the assigned segments of the set ofpotential occluded pedestrians over time using the assigned speedcharacteristics; receiving, by the one or more processors, sensor datafrom a perception system of the vehicle; identifying, by the one or moreprocessors, a segment of the set of segments that is visible to theperception system based on the sensor data; removing, by the one or moreprocessors, one or more potential occluded pedestrians having an updatedassigned segment corresponding to the identified segment from the set ofpotential occluded pedestrians; and after the removing, using, by theone or more processors, the set to control the vehicle in the autonomousdriving mode.

In one example, determining that the vehicle is approaching a crosswalkis based on a current route of the vehicle and map informationidentifying a location of the crosswalk. In another example, thegenerating is performed when the vehicle has reached a predetermineddistance. In this example, the predetermined distance corresponds to adistance from the crosswalk along a current route of the vehicle.Alternatively, the predetermined distance corresponds to a forward edgeof a field of view of a perception system of the vehicle including oneor more sensors. In another example, identifying the segments includessegmenting an area of the crosswalk into a predetermined number ofsegments. In another example, identifying the segments includesretrieving the segments from pre-stored map information. In anotherexample, generating the set of potential occluded pedestrians includesassociating each segment with a plurality of pedestrians havingdifferent speed characteristics. In this example, one of the differentspeed characteristics indicates that a pedestrian is stationary. Inaddition or alternatively, one of the different speed characteristicsincludes a pedestrian moving in a first direction towards a first edgeof the crosswalk at a first speed, and wherein another of the differentspeed characteristics includes a pedestrian moving in a second directionopposite of the first direction towards a second edge of the crosswalkopposite of the first edge at the first speed. In another example, theone or more potential occluded pedestrians are removed from the setbased on a determination that the one or more potential occludedpedestrians would have left the crosswalk. In another example, the oneor more potential occluded pedestrians are removed from the set is alsobased on whether an area corresponding to a current segment for that oneor more potential occluded pedestrians has been visible to theperception system for a minimum number of iterations of received sensordata while the one or more potential occluded pedestrians would haveoccupied the current segment. In another example, the one or morepotential occluded pedestrians are removed from the set is also based onwhether an area corresponding to a current segment for that one or morepotential occluded pedestrians has been visible to the perception systemfor at least a predetermined period of time during which the one or morepotential occluded pedestrians would have occupied the current segment.In another example, the one or more potential occluded pedestrians areremoved from the set is also based on whether a current segment for thatone or more potential occluded pedestrians has been occupied by anon-pedestrian object for a minimum number of iterations of receivedsensor data while the one or more potential occluded pedestrians wouldhave occupied the current segment. In another example, using the set ofpotential occluded pedestrians to control the vehicle in the autonomousdriving mode includes, generating a speed constraint based on the set ofpotential occluded pedestrians, the speed constraint which limits thespeed of the vehicle based on a distance of the vehicle from thecrosswalk to ensure that the vehicle is able to stop before reaching thecrosswalk. In another example, the method also includes determiningwhether one or more edges of the crosswalk are occluded and adding oneor more additional potential occluded pedestrians to the set ofpotential occluded pedestrians based on the determination of whether oneor more edges of the crosswalk are occluded, and wherein the removing isperformed after the adding. In this example, wherein when the one ormore edges of the crosswalk are not occluded, the one or more additionalpotential occluded pedestrians are not added to the set. In addition oralternatively, each of the one or more additional potential occludedpedestrians is assigned a speed characteristic. In another example,wherein using the set of potential occluded pedestrians to control thevehicle in the autonomous driving mode includes generating a speedconstraint based on the set of potential occluded pedestrians, the speedconstraint identifies a maximum speed limit for the vehicle whenapproaching the crosswalk. In this example, the speed constraint is aslow region and is generated based on a distance between one of the setof potential occluded pedestrians and the vehicle as well as theassigned speed characteristic for the one of the set of potentialoccluded pedestrians.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a functional diagram of an example vehicle in accordance withan exemplary embodiment.

FIG. 2 is an example of map information in accordance with aspects ofthe disclosure.

FIG. 3 is an example external view of a vehicle in accordance withaspects of the disclosure.

FIG. 4 is an example of a section of roadway corresponding to the mapinformation of FIG. 2 in accordance with aspects of the disclosure.

FIG. 5 is an example representation of a plurality of segments for asidewalk in accordance with aspects of the disclosure.

FIG. 6 is an example representation of a plurality of segments for asidewalk and a set of potential occluded pedestrians in accordance withaspects of the disclosure.

FIG. 7 is an example representation of a plurality of segments for asidewalk and set of potential occluded pedestrians in accordance withaspects of the disclosure.

FIG. 8 is an example representation of a plurality of segments for asidewalk and set of potential occluded pedestrians in accordance withaspects of the disclosure.

FIG. 9 is an example representation of a plurality of segments for asidewalk and set of potential occluded pedestrians in accordance withaspects of the disclosure.

FIG. 10 is an example flow diagram in accordance with aspects of thedisclosure.

DETAILED DESCRIPTION Overview

The technology relates to detecting potentially occluded objects, andparticularly those that may be located within or proximate to acrosswalk. For example, autonomous vehicles may travel on roadways whichinclude crosswalks. In many examples, various objects such as moving orparked vehicles or trees may cause parts of the crosswalk to beoccluded, or not directly visible to the autonomous vehicle's sensors.Because of this, the autonomous vehicle's computers may estimate whethera segment of a crosswalk could be occupied by an occluded object and mayuse this to control the vehicle.

As a vehicle drives around, it may at times approach and eventuallycrossover crosswalks. These crosswalks may be known apriori, that is,prestored in map information accessible by the vehicle's computingdevices. In this regard, the vehicle's computing devices may determinethat the vehicle is approaching a crosswalk. In other words, thisdetermination may be made before perception system is actually able toperceive the crosswalk.

Once the computing devices determine that the vehicle is approaching aparticular crosswalk, the computing devices may determine a plurality ofsegments for that crosswalk. These segments may be pre-stored, forinstance in the map information, and associated with the particularcrosswalk. Alternatively, the computing devices may generate theplurality of segments in real time.

A set of potential occluded pedestrians may be generated for eachsegment. These potential occluded pedestrians for each segment may begiven a different speed characteristic. This speed characteristic caninclude information such as the potential velocity and heading of theoccluded pedestrian. In some more complicated examples, the speedcharacteristic for a potential occluded pedestrian may include a patternof changing speeds or accelerations.

Overtime, with the exception of the stationary pedestrian, thepedestrians of the set will move into different segments of thecrosswalk and in some cases may actually exit the crosswalk. Thus, thecomputing devices may update the segments or the location of eachpotential occluded pedestrian of the set as time progresses forward.

As time progresses forward, the vehicle's perception system may providesensor data to the computing devices. The sensor data may identifyobjects such as pedestrians, bicyclists, and vehicles in and around thecrosswalk and various other locations. In addition, the sensor data mayallow the computing devices to identify which areas of the vehicle'senvironment are unoccupied or not occluded.

The computing devices may use this sensor data to determine whether anyof the potential occluded pedestrians of the set for each segment couldnot actually exist or rather, should be discarded or removed from theset. For instance, if a segment has been visible (or perceived by) for aminimum number of iterations or period of time and not occupied by adetected pedestrian, any potential occluded pedestrians which have beentracked (i.e. updated) to that segment may be removed from the set. Insome instances, potential occluded pedestrians may be removed from thesets because they may have left the crosswalk, for instance, by crossingone of the outer edges of the edge segments of the crosswalk. Similarly,if the area around the edge of the crosswalk is occluded, new potentialoccluded pedestrians may be added to the sets as potential occludedpedestrians who have entered the crosswalks. Again, these pedestriansthat enter the crosswalk may be given different speed characteristics asdescribed above. Of course, if the outer edges of the crosswalk are notoccluded new potential occluded pedestrians would not be added to theset.

As the segments become visible and potentially even occluded by objects,the number of potential occluded pedestrians in the set may change, forinstance, increasing as potential occluded pedestrians are added anddecreasing as potential occluded pedestrians are removed. At the sametime, after determining that the vehicle is approaching the crosswalk,the computing devices may also determine how to control the vehicleusing the potential occluded pedestrians of the set. Alternatively,rather than having the computing devices respond to a large number ofpotential occluded pedestrians, for each segment which is “occupied” byat least one potential occluded pedestrian, the computing devices maygenerate a speed constraint for the vehicle passing through thecrosswalk. The speed constraint may be determined based on the distanceof an occupied segment to the vehicle as well as the speed of potentialoccluded pedestrians in the occupied segment. The planning system maythen use the speed constraint when generating a speed plan for a futuretrajectory of the vehicle.

The features described herein may enable the vehicle's computing devicesto respond to potential occluded pedestrians who may be occluded whileat the same time reducing the likelihood and necessity for abruptmaneuvers in the event that a pedestrian who was previously occludedappears and causes the vehicle to have to respond to that pedestrian. Inaddition, the processing described herein is not computationallyintensive and can be easily recomputed when updates from sensor data areavailable.

Example Systems

As shown in FIG. 1 , a vehicle 100 in accordance with one aspect of thedisclosure includes various components. While certain aspects of thedisclosure are particularly useful in connection with specific types ofvehicles, the vehicle may be any type of vehicle including, but notlimited to, cars, trucks, motorcycles, buses, recreational vehicles,etc. The vehicle may have one or more computing devices, such ascomputing device 110 containing one or more processors 120, memory 130and other components typically present in general purpose computingdevices.

The memory 130 stores information accessible by the one or moreprocessors 120, including instructions 132 and data 134 that may beexecuted or otherwise used by the processor 120. The memory 130 may beof any type capable of storing information accessible by the processor,including a computing device-readable medium, or other medium thatstores data that may be read with the aid of an electronic device, suchas a hard-drive, memory card, ROM, RAM, DVD or other optical disks, aswell as other write-capable and read-only memories. Systems and methodsmay include different combinations of the foregoing, whereby differentportions of the instructions and data are stored on different types ofmedia.

The instructions 132 may be any set of instructions to be executeddirectly (such as machine code) or indirectly (such as scripts) by theprocessor. For example, the instructions may be stored as computingdevice code on the computing device-readable medium. In that regard, theterms “instructions” and “programs” may be used interchangeably herein.The instructions may be stored in object code format for directprocessing by the processor, or in any other computing device languageincluding scripts or collections of independent source code modules thatare interpreted on demand or compiled in advance. Functions, methods androutines of the instructions are explained in more detail below.

The data 134 may be retrieved, stored or modified by processor 120 inaccordance with the instructions 132. For instance, although the claimedsubject matter is not limited by any particular data structure, the datamay be stored in computing device registers, in a relational database asa table having a plurality of different fields and records, XMLdocuments or flat files. The data may also be formatted in any computingdevice-readable format.

The one or more processor 120 may be any conventional processors, suchas commercially available CPUs or GPUs. Alternatively, the one or moreprocessors may be a dedicated device such as an ASIC or otherhardware-based processor. Although FIG. 1 functionally illustrates theprocessor, memory, and other elements of computing device 110 as beingwithin the same block, it will be understood by those of ordinary skillin the art that the processor, computing device, or memory may actuallyinclude multiple processors, computing devices, or memories that may ormay not be stored within the same physical housing. For example, memorymay be a hard drive or other storage media located in a housingdifferent from that of computing device 110. Accordingly, references toa processor or computing device will be understood to include referencesto a collection of processors or computing devices or memories that mayor may not operate in parallel.

In one aspect the computing devices 110 may be part of an autonomouscontrol system capable of communicating with various components of thevehicle in order to control the vehicle in an autonomous driving mode.For example, returning to FIG. 1 , the computing devices 110 may be incommunication with various systems of vehicle 100, such as decelerationsystem 160, acceleration system 162, steering system 164, routing system166, planning system 168, positioning system 170, and perception system172 in order to control the movement, speed, etc. of vehicle 100 inaccordance with the instructions 132 of memory 130 in the autonomousdriving mode.

As an example, computing devices 110 may interact with decelerationsystem 160 and acceleration system 162 in order to control the speed ofthe vehicle. Similarly, steering system 164 may be used by computingdevices 110 in order to control the direction of vehicle 100. Forexample, if vehicle 100 is configured for use on a road, such as a caror truck, the steering system may include components to control theangle of wheels to turn the vehicle.

Planning system 168 may be used by computing devices 110 in order todetermine and follow a route generated by a routing system 166 to alocation. For instance, the routing system 166 may use map informationto determine a route from a current location of the vehicle to a dropoff location. The planning system 168 may periodically generatetrajectories, or short-term plans for controlling the vehicle for someperiod of time into the future, in order to follow the route (a currentroute of the vehicle) to the destination. In this regard, the planningsystem 168, routing system 166, and/or data 134 may store detailed mapinformation, e.g., highly detailed maps identifying the shape andelevation of roadways, lane lines, intersections, crosswalks, speedlimits, traffic signals, buildings, signs, real time trafficinformation, vegetation, or other such objects and information.

FIG. 2 is a high-level example of map information 200 for a roadwayincluding an intersection. In this example, the map information 200includes a plurality of different features that identify the shape andlocation of various features such as curbs or fog lines 210, 212, lanelines 220, 222, lanes 230, 232, crosswalks 240, 242, 244, andintersection 250. The map information 200 may be a part of the detailedmaps described above and used by the various computing devices ofvehicle 100 in order to maneuver the vehicle 100. In this regard, themap information may include significantly more detail than highlightedhere.

Although the map information is depicted herein as an image-based map,the map information need not be entirely image based (for example,raster). For example, the map information may include one or moreroadgraphs or graph networks of information such as roads, lanes,intersections, and the connections between these features which may berepresented by road segments. Each feature may be stored as graph dataand may be associated with information such as a geographic location andwhether or not it is linked to other related features, for example, astop sign may be linked to a road and an intersection, etc. In someexamples, the associated data may include grid-based indices of aroadgraph to allow for efficient lookup of certain roadgraph features.

Positioning system 170 may be used by computing devices 110 in order todetermine the vehicle's relative or absolute position on a map or on theearth. For example, the positioning system 170 may include a GPSreceiver to determine the device's latitude, longitude and/or altitudeposition. Other location systems such as laser-based localizationsystems, inertial-aided GPS, or camera-based localization may also beused to identify the location of the vehicle. The location of thevehicle may include an absolute geographical location, such as latitude,longitude, and altitude as well as relative location information, suchas location relative to other cars immediately around it which can oftenbe determined with less noise than absolute geographical location.

The positioning system 170 may also include other devices incommunication with the computing devices of the computing devices 110,such as an accelerometer, gyroscope or another direction/speed detectiondevice to determine the direction and speed of the vehicle or changesthereto. By way of example only, an acceleration device may determineits pitch, yaw or roll (or changes thereto) relative to the direction ofgravity or a plane perpendicular thereto. The device may also trackincreases or decreases in speed and the direction of such changes. Thedevice's provision of location and orientation data as set forth hereinmay be provided automatically to the computing device 110, othercomputing devices and combinations of the foregoing.

The perception system 172 also includes one or more components fordetecting objects external to the vehicle such as other vehicles,obstacles in the roadway, traffic signals, signs, trees, etc. Forexample, the perception system 172 may include lasers, sonar, radar,cameras and/or any other detection devices that record data which may beprocessed by the computing devices of the computing devices 110. In thecase where the vehicle is a passenger vehicle such as a minivan, theminivan may include a laser or other sensors mounted on the roof orother convenient location. For instance, FIG. 3 is an example externalview of vehicle 100. In this example, roof-top housing 310 and domehousing 312 may include a LIDAR sensor as well as various cameras andradar units. In addition, housing 320 located at the front end ofvehicle 100 and housings 330, 332 on the driver's and passenger's sidesof the vehicle may each store a LIDAR sensor. For example, housing 330is located in front of driver door 360. Vehicle 100 also includeshousings 340, 342 for radar units and/or cameras also located on theroof of vehicle 100. Additional radar units and cameras (not shown) maybe located at the front and rear ends of vehicle 100 and/or on otherpositions along the roof or roof-top housing 310. FIG. 3 also depictsleft and right turn signals 112, 114. In this example, front left turnsignal 112A, rear left turn signal 112B, and front right turn signal114A are depicted, but a right rear turn signal is not visible (or notperceived) from the perspective of FIG. 3 .

The computing devices 110 may be capable of communicating with variouscomponents of the vehicle in order to control the movement of vehicle100 according to primary vehicle control code of memory of the computingdevices 110. For example, returning to FIG. 1 , the computing devices110 may include various computing devices in communication with varioussystems of vehicle 100, such as deceleration system 160, accelerationsystem 162, steering system 164, routing system 166, planning system168, positioning system 170, perception system 172, and power system 174(i.e. the vehicle's engine or motor) in order to control the movement,speed, etc. of vehicle 100 in accordance with the instructions 132 ofmemory 130.

The various systems of the vehicle may function using autonomous vehiclecontrol software in order to determine how to and to control thevehicle. As an example, a perception system software module of theperception system 172 may use sensor data generated by one or moresensors of an autonomous vehicle, such as cameras, LIDAR sensors, radarunits, sonar units, etc., to detect and identify objects and theircharacteristics. These characteristics may include location, type,heading, orientation, speed, acceleration, change in acceleration, size,shape, etc. In some instances, characteristics may be input into abehavior prediction system software module which uses various behaviormodels based on object type to output a predicted future behavior for adetected object. In other instances, the characteristics may be put intoone or more detection system software modules, such as a traffic lightdetection system software module configured to detect the states ofknown traffic signals, construction zone detection system softwaremodule configured to detect construction zones from sensor datagenerated by the one or more sensors of the vehicle as well as anemergency vehicle detection system configured to detect emergencyvehicles from sensor data generated by sensors of the vehicle. Each ofthese detection system software modules may uses various models tooutput a likelihood of a construction zone or an object being anemergency vehicle. Detected objects, predicted future behaviors, variouslikelihoods from detection system software modules, the map informationidentifying the vehicle's environment, position information from thepositioning system 170 identifying the location and orientation of thevehicle, a destination for the vehicle as well as feedback from variousother systems of the vehicle may be input into a planning systemsoftware module of the planning system 168. The planning system may usethis input to generate trajectories for the vehicle to follow for somebrief period of time into the future based on a current route of thevehicle generated by a routing module of the routing system 166. Acontrol system software module of the computing devices 110 may beconfigured to control movement of the vehicle, for instance bycontrolling braking, acceleration and steering of the vehicle, in orderto follow a trajectory.

The computing devices 110 may control the vehicle in an autonomousdriving mode by controlling various components. For instance, by way ofexample, the computing devices 110 may navigate the vehicle to adestination location completely autonomously using data from thedetailed map information and planning system 168. The computing devices110 may use the positioning system 170 to determine the vehicle'slocation and perception system 172 to detect and respond to objects whenneeded to reach the location safely. Again, in order to do so, computingdevice 110 may generate trajectories and cause the vehicle to followthese trajectories, for instance, by causing the vehicle to accelerate(e.g., by supplying fuel or other energy to the engine or power system174 by acceleration system 162), decelerate (e.g., by decreasing thefuel supplied to the engine or power system 174, changing gears, and/orby applying brakes by deceleration system 160), change direction (e.g.,by turning the front or rear wheels of vehicle 100 by steering system164), and signal such changes (e.g., by lighting turn signals 112 or 114of the signaling system). Thus, the acceleration system 162 anddeceleration system 160 may be a part of a drivetrain that includesvarious components between an engine of the vehicle and the wheels ofthe vehicle. Again, by controlling these systems, computing devices 110may also control the drivetrain of the vehicle in order to maneuver thevehicle autonomously.

Example Methods

In addition to the operations described above and illustrated in thefigures, various operations will now be described. It should beunderstood that the following operations do not have to be performed inthe precise order described below. Rather, various steps can be handledin a different order or simultaneously, and steps may also be added oromitted.

For the purposes of demonstration, FIG. 4 is an example representationof a section of roadway 400 corresponding to the map information 200. Inthis regard, the shapes and locations of curbs or fog lines 410, 412,lane lines 420, 422, lanes 430, 432, crosswalks 440, 442, 444, andintersection 450 may correspond to the shapes and locations of curbs orfog lines 210, 212, lane lines 220, 222, lanes 230, 232, crosswalks 240,242, 244, and intersection 250, respectively. In this example, vehicle100 is approaching the crosswalk 440 in lane 430. In addition, thesensors of the perception system 172 may also perceive vehicles 460(also approaching crosswalk 440 in lane 430) and vehicle 470 (movingaway from crosswalk 440 in lane 432). These vehicles 460, 470 mayprevent the sensors of the perception system 172 from perceiving all ofthe areas of crosswalk 440.

FIG. 10 is an example flow diagram 1000 in accordance with aspects ofthe disclosure which may be performed by one or more processors of oneor more computing devices, such as processors 120 of computing devices110, in order to maneuver a vehicle having an autonomous driving mode.As shown in block 1010, a vehicle having an autonomous driving mode isdetermined to be approaching a crosswalk. For instance, as a vehicledrives around, it may at times approach and eventually crossovercrosswalks. For example, computing devices 110 may use the mapinformation 200 to determine that the vehicle 100 is approaching thecrosswalk 440 or the perception system 172 may actually detect thecrosswalk 440 using the aforementioned sensors. The timing of thisdetermination may correspond to when the perception system 172 initiallydetects the crosswalk or when the vehicle is located a predetermineddistance (e.g. linear or driving distance) such as 170 meters or more orless from the crosswalk 440 along a current route of the vehicle (i.e.that the vehicle is currently traveling), for instance, throughintersection 450. In some examples this predetermined distance maycorrespond to a forward edge of or just beyond a field of view of thevehicle's perception system. In other words, this determination may bemade before perception system is actually able to perceive thecrosswalk.

Returning to FIG. 10 , a set of segments is identified for the crosswalkat block 1020. Once the computing devices determine that the vehicle isapproaching a particular crosswalk, the computing devices may determinea plurality of segments for that crosswalk. These segments may bepre-stored, for instance in the map information, and associated with theparticular crosswalk. Alternatively, the computing devices may generatethe plurality of segments in real time. As an example, a crosswalk maybe segmented into a predetermined number of segments, such as 5 segmentsor more or less, along the length of the crosswalk. In this regard, thecrosswalk may include 3 internal segments and 2 edge segmentscorresponding to the ends of the crosswalk. Alternatively, a crosswalkmay be segmented into segments of a predetermined length such as 3meters or more or less. FIG. 5 is an example representation of aplurality of segments 1-5 for crosswalk 440. In this example, thecrosswalk 440 is divided into 5 equal segments. Again, this is only anexample, and more segments of different shapes and/or sizes may be used.

Returning to FIG. 10 , at block 1030, a set of potential occludedpedestrians is generated, and each potential occluded pedestrian of theset is assigned a speed characteristic defining a speed of the potentialoccluded pedestrian and assigned a segment of the set of segments. Forinstance, a set of potential occluded pedestrians may be generated foreach segment 1-5 of the crosswalk 440. As an example, a list or otherset of data may be generated representing each pedestrian of the set ofpedestrians as well as a segment in which the pedestrian is currentlylocated.

These potential occluded pedestrians for each segment may be assigned orotherwise be associated with a different speed characteristic. Thisspeed characteristic can include information such as the potential speedand heading of the potential occluded pedestrian. As an example, thespeed could be an average or estimated speed of a typical pedestrianwalking slowly, “speed walking”, jogging, running, etc. In some morecomplicated examples, the speed characteristic for a potential occludedpedestrian may include a pattern of changing speeds or accelerations.Although the examples herein relate specifically to “pedestrians” theset of potential occluded pedestrians may actually be representative ofall types of potentially occluded objects in a crosswalk such asbicyclists, adults, children, pets (dogs, horses, etc.), baby carriagesor strollers, etc. with various speed characteristics.

As one representative example, if there are 11 potential occludedpedestrians for each segment, and there are 5 segments, there may be 55potential occluded pedestrians in the set. Referring to a singlesegment, one potential occluded pedestrian may be stopped, anotherpotential occluded pedestrian may be moving 1 mph towards a first edgeof the crosswalk, another potential occluded pedestrian may be moving 2mph towards the first edge of the crosswalk, another potential occludedpedestrian may be moving 3 mph towards the first edge of the crosswalk,another potential occluded pedestrian may be moving 6 mph towards thefirst edge of the crosswalk, another potential occluded pedestrian maybe moving 12 mph towards the first edge of the crosswalk, anotherpotential occluded pedestrian may be moving 1 mph towards a second edgeof the crosswalk, another potential occluded pedestrian may be moving 2mph towards the second edge of the crosswalk, another potential occludedpedestrian may be moving 3 mph towards the second edge of the crosswalk,another potential occluded pedestrian may be moving 6 mph towards thesecond edge of the crosswalk, and another potential occluded pedestrianmay be moving 12 mph towards the second edge of the crosswalk. Theseedges may be opposite of one another. Other speed characteristics (suchas those discussed above) and numbers of pedestrians may also bepossible.

FIG. 6 is an example representation of segments 1-5 each and a set ofpotential occluded pedestrians A-O. In this example, each of thesegments 1-5 is associated with three potential occluded pedestrians:one potential occluded pedestrian having a speed of 2 miles per hour toan edge of the crosswalk at the left side of the page (potentialoccluded pedestrians A, D, G, J, and M), one potential occludedpedestrian who is stationary (potential occluded pedestrians B, E, H, K,and N, moving at O mph), and one potential occluded pedestrian having aspeed of 2 miles per hour to an edge of the crosswalk at the right sideof the page (potential occluded pedestrians C, F, I, L, and O. Again, asnoted above, additional potential occluded pedestrians with differentspeeds or other speed characteristics may also be possible. In thisexample, potential occluded pedestrians A-C are located in segment 1,potential occluded pedestrians D-F are located in segment 2, potentialoccluded pedestrians G-I are located in segment 3, potential occludedpedestrians J-L are located in segment 4, and potential occludedpedestrians M-O are located in segment 5.

Returning to FIG. 10 , at block 1040, the assigned segments of the setof potential occluded pedestrians may be updated over time based on theassigned speed characteristics. In other words, as time progressesforward, with the exception of the stationary pedestrian, thepedestrians of the set will move into different segments of thecrosswalk and in some cases may actually exit the crosswalk. Thus, thecomputing devices may update the segment or the location of eachpotential occluded pedestrian of the set as time progresses forward.These updates may be periodic, for instance, every 0.1 seconds or moreor less, in order to keep the assigned segments consistent with wherethe potential occluded pedestrians would be at the current time. Theperiod for updating may also be commensurate with or dictated by howoften sensor data is generated by the perception system and/or receivedby the computing devices 100. For instance, if updated sensor data isreceived every 0.1 seconds or more or less, the assigned segments may beupdated every 0.1 seconds or more or less. In this way, the assignedsegments and the sensor data being analyzed by the computing devices 110are both time synchronized. Alternatively, as sensor data is received,the assigned segments may be updated to the time for or at which thesensor data was generated.

FIG. 7 is an example representation of segments 1-5 each and a set ofpotential occluded pedestrians A-O at some point in the future withrespect to the example of FIG. 6 . In this example, potential occludedpedestrians D, G, J, and M have moved one segment to the left of thepage, and potential occluded pedestrian A is not in any of the segments1-5 or rather, is no longer located within a segment in the crosswalk.As such, potential occluded pedestrian A is shown in dashed-line.Potential occluded pedestrians C, F, I, and L have moved one segment tothe left of the page, and potential occluded pedestrian O is not in anyof the segments 1-5 or rather, is no longer located within a segment inthe crosswalk. As such, potential occluded pedestrian O is shown indashed-line. Potential occluded pedestrians B, E, H, K, and N remain ineach of segments 1-5, respectively.

Returning to FIG. 10 , at block 1050, sensor data is received from aperception system of the vehicle, and at block 1060, a segment of theset of segments that is visible to the perception system is identifiedbased on the sensor data. As time progresses forward, the perceptionsystem 172 may provide sensor data to the computing devices 110. Asnoted above, this sensor data may be received every 0.1 seconds or moreor less and may be commensurate with the updates to the assignedsegments. Each time the sensor data is received may be considered a newiteration of sensor data from the perception system. The sensor data mayidentify objects such as pedestrians, bicyclists, and vehicles in andaround the crosswalk and various other locations. In addition, thesensor data may allow the computing devices to identify which areas ofthe vehicle's environment are unoccupied or not occluded, includingareas corresponding to the segments of the set of segments.

At block 1070 of FIG. 10 , one or more potential occluded pedestrianshaving an updated assigned segment corresponding to the identifiedsegment is removed from the set of potential occluded pedestrians. Thecomputing devices 110 may use this sensor data to determine whether anyof the potential occluded pedestrians of the set for each segment couldnot actually exist or rather, should be discarded, filtered or otherwiseremoved from the set. For instance, if a segment has been visible for aminimum number of iterations or period of time and not occupied by adetected pedestrian, any potential occluded pedestrians which have beentracked (i.e. updated) to that segment may be removed from the set. Asan example, this minimum number of iterations may be at least 5 (or moreor less) iterations of sensor data from the perception system. Asanother example, the minimum number of iterations may be at least 0.5seconds or more or less. If a section of the crosswalk has been occupiedby an object other than a pedestrian (e.g. a non-pedestrian object) fora minimum number of iterations, for example more than 3 (or more orless) iterations of sensor data received from the perception system, ora minimum period of time, for example, for at least 0.3 seconds or moreor less, any potential occluded pedestrians which have been tracked(i.e. updated) to that segment may be removed from the set.

FIG. 8 is an example representation of segments 1-5 each and a set ofpotential occluded pedestrians A-O at some point in the future withrespect to the example of FIG. 6 corresponding to the time of FIG. 7 andthe location of vehicle 100 in FIG. 9 . In the example of FIG. 9 , thevehicle 100 as well as vehicle 460 have moved closer to the crosswalk440 and vehicle 470 has moved away from the crosswalk 440, enabling thesensors of the perception system 172 to perceive portions of thecrosswalk 440. In this example, the sensor data from the perceptionsystem 172 may indicate that the areas of the crosswalk 440corresponding to segments 1-3 may be detected or perceived by theperception system 172, and are therefore not occluded. In this regard,the potential occluded pedestrians D, B, C, E, G, F, H, and J (now shownin dashed-line) may be removed from the set of potential occludedpedestrians.

In some instances, potential occluded pedestrians may be removed fromthe sets because they may have left the crosswalk, for instance, bycrossing one of the outer edges of the edge segments of the crosswalk.In addition, because the potential occluded pedestrians A and O (shownin dashed-line in FIGS. 7 and 8 ) are no longer located within a segmentof the plurality of segments, these potential occluded pedestrians mayalso be removed from the set of potential occluded pedestrians.

Similarly, if the area around the edge of the crosswalk is occluded, newpotential occluded pedestrians may be added to the sets as potentialoccluded pedestrians who have entered the crosswalks. Again, thesepedestrians that enter the crosswalk may be given different speedcharacteristics as described above. Of course, if the outer edges of thecrosswalk are not occluded new potential occluded pedestrians would notbe added to the set.

Returning to FIG. 10 , at block 1080, after the removing, the set isused to control the vehicle in the autonomous driving mode. As thesegments (or rather, the areas of the segments of the crosswalk) becomevisible to the perception system and/or even occluded by other objects,the number of potential occluded pedestrians in the set may change, forinstance, increasing as potential occluded pedestrians are added anddecreasing as potential occluded pedestrians are removed. At the sametime, after determining that the vehicle is approaching the crosswalk,the computing devices may also determine how to control the vehicleusing the potential occluded pedestrians of the set. This may be done inany number of ways. As an example, each potential occluded pedestrianmay be input into a planning system of the vehicle as an object to whichthe vehicle should respond.

Alternatively, rather than having the computing devices respond to alarge number of potential occluded pedestrians, for each segment whichis “occupied” by at least one potential occluded pedestrian, thecomputing devices may generate a speed constraint for the vehiclepassing through the crosswalk. In one example, the speed constraint maybe a limit on the speed of the vehicle that is determined based on adistance of the vehicle from the crosswalk to ensure that the vehicle isable to stop before reaching the crosswalk. In another example, thespeed constraint may correspond to a slow region, for example, an areathat has a limit on a maximum speed of the vehicle as it passes throughor by the occupied segment or segments. In this regard, the planningsystem 168 must obey the speed constraint and/or maximum speed limit forthe slow region when planning the vehicle's speed through the crosswalk.Returning to the example of FIG. 8 , at this point in time, thecomputing devices 110 may generate a speed restriction with regard tothe area of the crosswalk 440 corresponding to segments 4 and 5corresponding to area 910. The area 920 may correspond to an areathrough which a volume of the vehicle 100 will sweep while approachingthe area 910. In this regard, areas 910 and/or 920 of FIG. 9 mayrepresent an area for a speed constraint or a slow region through whichthe vehicle 100 can travel at or below the maximum speed limitassociated with the areas 910 and/or 920.

The speed constraint and/or slow region may be determined by thecomputing devices 110 based on the distance of an occupied segment tothe vehicle as well as the speed of potential occluded pedestrians inthe occupied segment. For example, the closer the locations or segmentsof the potential occluded pedestrians are to the vehicle, the lower thespeed constraint or the lower the limit on the maximum speed). Forexample, if an occupied segment corresponds to an adjacent lane, thelimit may be 5 mph or more or less. As another example, if an occupiedsegment corresponds to two lanes over, the limit may be 15 mph or moreor less. Again, the speed constraint may also be based on a speed of thefastest potential occluded pedestrian of the set that occupies asegment. In other words, A pedestrian that is 2 lanes away from thevehicle and who is traveling at 12 mph, may create a much more dangeroussituation as compared to a pedestrian in an adjacent lane who isstopped. In the faster potential occluded pedestrian case, the slowregion may be slower than for the stopped potential occluded pedestrianeven though the stopped potential occluded pedestrian is closer. At thesame time, if a segment is occupied by 5 potential occluded pedestrians,the speed constraint or the limit on the maximum speed (and thereforethe vehicle's response) would be the same as if 1 potential occludedpedestrian of the fastest speed towards the vehicle occupied thesegment.

The planning system 168 may then use the slow region when generating aspeed plan for a future trajectory of the vehicle 100. Because thetracking of potential occluded pedestrians may begin before or as soonas the vehicle is able to detect the crosswalk, the slow region maycorrespond to a location that actually allows the vehicle to stop. Inother words, the distance between the vehicle and the slow region may beless than a current stopping distance for the vehicle given thevehicle's current speed and assuming a reasonable rate of deceleration.In this way, the slow region is not a “surprise” which results in thevehicle breaking uncomfortably hard. In some instances, a slow regionmay be violated, for instance, in an emergency situation where a vehiclebehind the vehicle is approaching at a high rate of speed, etc.

The features described herein may enable the vehicle's computing devicesto respond to potential occluded pedestrians who may be occluded whileat the same time reducing the likelihood and necessity for abruptmaneuvers in the event that a pedestrian who was previously occludedappears and causes the vehicle to have to respond to that pedestrian. Inaddition, the processing described herein is not computationallyintensive and can be easily recomputed when updates from sensor data areavailable.

Unless otherwise stated, the foregoing alternative examples are notmutually exclusive, but may be implemented in various combinations toachieve unique advantages. As these and other variations andcombinations of the features discussed above can be utilized withoutdeparting from the subject matter defined by the claims, the foregoingdescription of the embodiments should be taken by way of illustrationrather than by way of limitation of the subject matter defined by theclaims. In addition, the provision of the examples described herein, aswell as clauses phrased as “such as,” “including” and the like, shouldnot be interpreted as limiting the subject matter of the claims to thespecific examples; rather, the examples are intended to illustrate onlyone of many possible embodiments. Further, the same reference numbers indifferent drawings can identify the same or similar elements.

1. A method comprising: receiving, by one or more processors and from aperception system of a vehicle, sensor data corresponding to acrosswalk; updating, by the one or more processors and for thecrosswalk, locations of a set of potential occluded objects based atleast on speed characteristics of the set of potential occluded objects;updating, by the one or more processors, the set of potential occludedobjects based at least on an observation of another vehicle with respectto the crosswalk; and controlling the vehicle in an autonomous drivingmode, by the one or more processors, based at least on the updated setof potential occluded objects.
 2. The method of claim 1, whereinupdating the set of potential occluded objects comprises adding, by theone or more processors, one or more additional potential occludedobjects to the set of potential occluded objects in response to thesensor data being indicative of the other vehicle has entered thecrosswalk
 3. The method of claim 1, wherein updating the set ofpotential occluded objects comprises removing, by the one or moreprocessors, one or more potential occluded objects from the set ofpotential occluded objects in response to the sensor data beingindicative of the other vehicle has left the crosswalk.
 4. The method ofclaim 1, wherein the set of potential occluded objects does not includethe other vehicle.
 5. The method of claim 1, wherein updating the set ofpotential occluded pedestrians is further based on the observation ofthe other vehicle with respect to the crosswalk for a minimum number ofiterations of the sensor data.
 6. The method of claim 1, wherein theobservation of the other vehicle is indicative of the other vehicleoccupying one of the locations of the set of potential occluded objects.7. The method of claim 1, further comprising, in response to the vehiclebeing within a predetermined distance from the crosswalk: assigning, bythe one or more processors and based on the sensor data, the speedcharacteristics to the set of potential occluded objects, wherein thespeed characteristics define respective speeds of the set of potentialoccluded objects; and assigning, by the one or more processors and basedon the sensor data, the locations of the set of potential occludedobjects.
 8. The method of claim 7, wherein the predetermined distancecorresponds to a distance between the vehicle and a forward edge of afield of view of the perception system.
 9. The method of claim 1,further comprising identifying, by the one or more processors and basedon the sensor data, one or more potential occluded objects of the set ofpotential occluded objects that have left the crosswalk, and whereinupdating the set of potential occluded objects comprises removing theidentified one or more potential occluded objects from the set ofpotential occluded objects.
 10. The method of claim 1, furthercomprising determining whether one or more edges of the crosswalk areoccluded, and wherein updating the set of potential occluded objectscomprises adding one or more additional potential occluded objects tothe set of potential occluded objects in response to determining thatthe one or more edges of the crosswalk are occluded.
 11. A systemcomprising: a perception system of a vehicle; and one or more processorscoupled to the perception system and configured to: receive, from theperception system, sensor data corresponding to a crosswalk; update, forthe crosswalk, locations of a set of potential occluded objects based onspeed characteristics of the set of potential occluded objects; updatethe set of potential occluded objects based at least on an observationof another vehicle with respect to the crosswalk; and control thevehicle in an autonomous driving mode based at least on the updated setof potential occluded objects.
 12. The system of claim 11, wherein theone or more processors are further configured to add one or moreadditional potential occluded objects to the set of potential occludedobjects in response to the sensor data being indicative of the othervehicle has entered the crosswalk
 13. The system of claim 11, whereinthe one or more processors are further configured to remove one or morepotential occluded objects from the set of potential occluded objects inresponse to the sensor data being indicative of the other vehicle hasleft the crosswalk.
 14. The system of claim 11, wherein the set ofpotential occluded objects does not include the other vehicle.
 15. Thesystem of claim 11, wherein the one or more processors are furtherconfigured to update the set of potential occluded pedestrians based onthe observation of the other vehicle with respect to the crosswalk for aminimum number of iterations of the sensor data.
 16. The system of claim11, wherein the observation of the other vehicle is indicative of theother vehicle occupying one of the locations of the set of potentialoccluded objects.
 17. The system of claim 11, wherein the one or moreprocessors are further configured to, in response to the vehicle beingwithin a predetermined distance from the crosswalk: assign, based on thesensor data, the speed characteristics to the set of potential occludedobjects, wherein the speed characteristics define respective speeds ofthe set of potential occluded objects; and assign, based on the sensordata, the locations of the set of potential occluded objects.
 18. Thesystem of claim 17, wherein the predetermined distance corresponds to adistance between the vehicle and a forward edge of a field of view ofthe perception system.
 19. The system of claim 11, wherein the one ormore processors are further configured to: identify, based on the sensordata, one or more potential occluded objects of the set of potentialoccluded objects that have left the crosswalk; and remove the identifiedone or more potential occluded objects from the set of potentialoccluded objects.
 20. The system of claim 11, wherein the one or moreprocessors are further configured to: determine whether one or moreedges of the crosswalk are occluded; and add one or more additionalpotential occluded objects to the set of potential occluded objects inresponse to determining that the one or more edges of the crosswalk areoccluded.